Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "145" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460009 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.037387 | -0.653413 | 2.762049 | -0.188519 | 0.315837 | 5.635799 | 0.018686 | -0.688933 | 0.6302 | 0.6566 | 0.3510 | nan | nan |
| 2460008 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.173577 | -0.600199 | 2.529756 | 0.602222 | 0.322312 | 2.542085 | 1.932510 | 0.154200 | 0.6762 | 0.6896 | 0.3152 | nan | nan |
| 2460007 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.063525 | -0.424110 | 2.041776 | 0.747810 | 0.113373 | 1.092028 | 0.662941 | -1.631584 | 0.6426 | 0.6639 | 0.3370 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.75% | 99.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.4105 | 0.3556 | 0.2321 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.285445 | -0.483947 | 1.839457 | 0.574799 | 0.007484 | 4.770653 | 0.217796 | -0.075678 | 0.6221 | 0.6505 | 0.3762 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.006226 | -0.456137 | 3.035977 | -0.459582 | 0.440173 | 5.313930 | 0.004855 | -0.528351 | 0.6348 | 0.6591 | 0.3953 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.406708 | -0.511064 | 2.116957 | 0.363518 | 0.117716 | 8.568281 | -0.040063 | -0.948313 | 0.6320 | 0.6589 | 0.3773 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.582585 | -0.193137 | 1.758510 | 0.843022 | 0.226138 | 2.226270 | 0.123485 | -0.815658 | 0.6286 | 0.6505 | 0.3682 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.849369 | -0.132487 | 1.405008 | 1.159054 | 0.082566 | 2.570449 | 0.085562 | -0.873833 | 0.6205 | 0.6540 | 0.3788 | nan | nan |
| 2459991 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.936161 | -0.002474 | 1.531852 | 1.222261 | -0.197080 | 3.019302 | -0.171228 | -1.246362 | 0.6357 | 0.6522 | 0.3743 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.584901 | 13.101427 | 9.796233 | 10.506089 | 8.844139 | 11.076158 | -0.015561 | 2.107912 | 0.0710 | 0.0310 | 0.0297 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.355778 | 13.300838 | 8.718229 | 9.601987 | 7.817299 | 9.295872 | 0.262578 | 1.992932 | 0.0684 | 0.0291 | 0.0287 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.434757 | 15.539005 | 10.103550 | 10.798747 | 10.543499 | 13.301593 | -0.081007 | 1.775407 | 0.0719 | 0.0291 | 0.0313 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 98.54% | 100.00% | 0.00% | - | - | 10.262699 | 13.046457 | 9.787055 | 10.646436 | 6.194673 | 7.978299 | 0.510932 | 3.792566 | 0.0764 | 0.0309 | 0.0345 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 99.95% | 100.00% | 0.00% | - | - | 12.811818 | 15.987788 | 10.719881 | 11.497566 | 9.418895 | 11.293896 | 5.330224 | 10.419789 | 0.0859 | 0.0300 | 0.0423 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 72.27% | 100.00% | 0.00% | - | - | 15.874524 | 14.436071 | 8.805109 | 10.713487 | 5.182641 | 8.633778 | 0.234332 | 3.849291 | 0.1899 | 0.0302 | 0.1267 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 62.74% | 100.00% | 0.00% | - | - | 15.774145 | 14.028727 | 9.087682 | 11.091909 | 7.622572 | 12.140309 | 2.170073 | 4.785875 | 0.2145 | 0.0325 | 0.1453 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 99.57% | 100.00% | 0.00% | - | - | 11.017770 | 13.658219 | 9.871064 | 10.507702 | 9.075204 | 11.220927 | 3.241908 | 7.653292 | 0.0809 | 0.0313 | 0.0381 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 90.76% | 100.00% | 0.00% | - | - | 9.229661 | 11.595316 | 8.389658 | 8.951189 | 4.372712 | 5.259938 | 2.292095 | 3.404643 | 0.1007 | 0.0301 | 0.0550 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.216636 | 12.532052 | 10.530389 | 11.185045 | 10.177579 | 12.370010 | -0.045440 | 2.240579 | 0.0753 | 0.0317 | 0.0329 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 96.16% | 100.00% | 0.00% | - | - | 10.057815 | 12.047723 | 9.456956 | 10.218724 | 8.773627 | 10.792819 | 4.906745 | 5.578744 | 0.0899 | 0.0323 | 0.0441 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.418660 | 12.618478 | 8.766793 | 9.562992 | 8.766196 | 10.159245 | -0.001840 | 2.148855 | 0.0663 | 0.0313 | 0.0255 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.524845 | 12.895225 | 9.520863 | 10.296008 | 9.136903 | 10.999241 | -0.276070 | 2.272718 | 0.0635 | 0.0296 | 0.0259 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.841902 | 13.579709 | 9.342531 | 10.144726 | 9.017124 | 11.349691 | 0.574758 | 3.201613 | 0.0748 | 0.0333 | 0.0319 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.743814 | 13.008803 | 9.839264 | 10.568995 | 9.171137 | 10.913895 | 0.570218 | 2.331996 | 0.0656 | 0.0307 | 0.0268 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 5.635799 | -0.037387 | -0.653413 | 2.762049 | -0.188519 | 0.315837 | 5.635799 | 0.018686 | -0.688933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 2.542085 | -0.600199 | -0.173577 | 0.602222 | 2.529756 | 2.542085 | 0.322312 | 0.154200 | 1.932510 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | ee Power | 2.041776 | -0.063525 | -0.424110 | 2.041776 | 0.747810 | 0.113373 | 1.092028 | 0.662941 | -1.631584 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 4.770653 | -0.285445 | -0.483947 | 1.839457 | 0.574799 | 0.007484 | 4.770653 | 0.217796 | -0.075678 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 5.313930 | 0.006226 | -0.456137 | 3.035977 | -0.459582 | 0.440173 | 5.313930 | 0.004855 | -0.528351 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 8.568281 | -0.406708 | -0.511064 | 2.116957 | 0.363518 | 0.117716 | 8.568281 | -0.040063 | -0.948313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 2.226270 | -0.582585 | -0.193137 | 1.758510 | 0.843022 | 0.226138 | 2.226270 | 0.123485 | -0.815658 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 2.570449 | -0.849369 | -0.132487 | 1.405008 | 1.159054 | 0.082566 | 2.570449 | 0.085562 | -0.873833 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Temporal Variability | 3.019302 | -0.936161 | -0.002474 | 1.531852 | 1.222261 | -0.197080 | 3.019302 | -0.171228 | -1.246362 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | digital_ok | nn Shape | 13.101427 | 13.101427 | 10.584901 | 10.506089 | 9.796233 | 11.076158 | 8.844139 | 2.107912 | -0.015561 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 13.300838 | 13.300838 | 10.355778 | 9.601987 | 8.718229 | 9.295872 | 7.817299 | 1.992932 | 0.262578 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 15.539005 | 15.539005 | 12.434757 | 10.798747 | 10.103550 | 13.301593 | 10.543499 | 1.775407 | -0.081007 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 13.046457 | 10.262699 | 13.046457 | 9.787055 | 10.646436 | 6.194673 | 7.978299 | 0.510932 | 3.792566 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 15.987788 | 15.987788 | 12.811818 | 11.497566 | 10.719881 | 11.293896 | 9.418895 | 10.419789 | 5.330224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | ee Shape | 15.874524 | 14.436071 | 15.874524 | 10.713487 | 8.805109 | 8.633778 | 5.182641 | 3.849291 | 0.234332 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | ee Shape | 15.774145 | 15.774145 | 14.028727 | 9.087682 | 11.091909 | 7.622572 | 12.140309 | 2.170073 | 4.785875 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 13.658219 | 11.017770 | 13.658219 | 9.871064 | 10.507702 | 9.075204 | 11.220927 | 3.241908 | 7.653292 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 11.595316 | 9.229661 | 11.595316 | 8.389658 | 8.951189 | 4.372712 | 5.259938 | 2.292095 | 3.404643 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 12.532052 | 12.532052 | 10.216636 | 11.185045 | 10.530389 | 12.370010 | 10.177579 | 2.240579 | -0.045440 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 12.047723 | 12.047723 | 10.057815 | 10.218724 | 9.456956 | 10.792819 | 8.773627 | 5.578744 | 4.906745 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 12.618478 | 10.418660 | 12.618478 | 8.766793 | 9.562992 | 8.766196 | 10.159245 | -0.001840 | 2.148855 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 12.895225 | 12.895225 | 10.524845 | 10.296008 | 9.520863 | 10.999241 | 9.136903 | 2.272718 | -0.276070 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 13.579709 | 10.841902 | 13.579709 | 9.342531 | 10.144726 | 9.017124 | 11.349691 | 0.574758 | 3.201613 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145 | N14 | RF_maintenance | nn Shape | 13.008803 | 13.008803 | 10.743814 | 10.568995 | 9.839264 | 10.913895 | 9.171137 | 2.331996 | 0.570218 |